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Length generalization refers to the ability to extrapolate from short training sequences to long test sequences and is a challenge for current large language models.
Computing machinery and intelligence
A. M. Turing · 1950
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Hava T Siegelmann and Eduardo D Sontag · 1992
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Primes is in p
Manindra Agrawal, Neeraj Kayal, and Nitin Saxena · 2004
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Łukasz Kaiser and Ilya Sutskever · 2015
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Recurrent neural networks as weighted language recognizers
Yining Chen, Sorcha Gilroy, Andreas Maletti, Jonathan May, and Kevin Knight · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Łukasz Kaiser · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2018
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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On the turing completeness of modern neural network architectures
Jorge Pérez, Javier Marinković, and Pablo Barceló · 2019
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On the computational power of transformers and its implications in sequence modeling
Satwik Bhattamishra, Arkil Patel, and Navin Goyal · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Compositionality decomposed: How do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Turing completeness of bounded-precision recurrent neural networks
Stephen Chung and Hava Siegelmann · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah A Smith, and Mike Lewis · 2021
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Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, and Tom Goldstein · 2021
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Thinking like transformers
Gail Weiss, Yoav Goldberg, and Eran Yahav · 2021
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Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur · 2022
Length generalization in arithmetic transformers
Samy Jelassi, Stéphane d’Ascoli, Carles Domingo-Enrich, Yuhuai Wu, Yuanzhi Li, and François Charton · 2023
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Gpt is becoming a turing machine: Here are some ways to program it
Ana Jojic, Zhen Wang, and Nebojsa Jojic · 2023
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Teaching arithmetic to small transformers
Nayoung Lee, Kartik Sreenivasan, Jason D Lee, Kangwook Lee, and Dimitris Papailiopoulos · 2023
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Functional interpolation for relative positions improves long context transformers
Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, and Srinadh Bhojanapalli · 2023
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Gpt-neox-20b: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al · 2022
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Kerple: Kernelized relative positional embedding for length extrapolation
Ta-Chung Chi, Ting-Han Fan, Peter J Ramadge, and Alexander Rudnicky · 2022
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Neural networks and the chomsky hierarchy
Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Chris Cundy, Marcus Hutter, Shane Legg, Joel Veness, et al · 2022
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Transformer language models without positional encodings still learn positional information
Adi Haviv, Ori Ram, Ofir Press, Peter Izsak, and Omer Levy · 2022
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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al · 2022
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Transformers learn shortcuts to automata
Bingbin Liu, Jordan T Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Eran Malach · 2023
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The expresssive power of transformers with chain of thought
William Merrill and Ashish Sabharwal · 2023
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Randomized positional encodings boost length generalization of transformers
Anian Ruoss, Grégoire Delétang, Tim Genewein, Jordi Grau-Moya, Róbert Csordás, Mehdi Bennani, Shane Legg, and Joel Veness · 2023
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Positional description matters for transformers arithmetic
Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Yuanzhi Li, and Yi Zhang · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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What algorithms can transformers learn? a study in length generalization
Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh Susskind, Samy Bengio, and Preetum Nakkiran · 2023
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Faith and fate: Limits of transformers on compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Sean Welleck, Peter West, Chandra Bhagavatula, Ronan Le Bras, et al · 2024
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Case-based or rule-based: How do transformers do the math?
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Repeat after me: Transformers are better than state space models at copying
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The impact of positional encoding on length generalization in transformers
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Learning to reason and memorize with self-notes
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Tracr: Compiled transformers as a laboratory for interpretability
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
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Transformers can achieve length generalization but not robustly
Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, and Denny Zhou · 2024
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